Neurological Imaging Order Selection Using Natural Language Processing and a Support Vector Classifier

Purpose To develop an algorithm for automated medical imaging order selection based on provider-input signs and symptoms using natural language processing and machine learning. The aim is to reduce the frequency of inappropriate physician imaging orders, which currently accounts for 25.7% of cases, and thereby mitigate potential patient health concerns. Materials and Methods The study was conducted retrospectively with a four-step analysis process. The data used for training in the study consisted of anonymized imaging records and associated provider-input symptoms for CT and MRI orders in 40,667 patients from a tertiary children’s hospital. First, the data were normalized using keyword filtering and lemmatization. Second, an entity-embedding ML model converted the symptoms to high-dimensional numerical vectors suitable for model comprehension, which we used to balance the dataset through k-nearest-neighbor-based synthetic sampling. Third, a Support Vector Classifier (ML model) was trained and hyperparameter-tuned using the embedded symptoms to predict modality (CT/MRI), contrast (with/without), and anatomical region (head, neck, etc.) for the imaging orders. Finally, a web application was developed to package the model, which analyzes user-input symptoms and outputs the predicted order. Results The model was found to have a final overall accuracy of 93.2% on a 4,704-case test set ( p < 0.001). The AUCs for the eight classes ranged from 96% to 100%, and the average F1-score was 0.92. Conclusion This algorithm looks to act as a clinical decision support tool to help augment the present physician imaging order selection accuracy and improve patient health. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Baylor College of Medicine and Affiliated Hospitals gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data provided in the manuscript is a private dataset from Texas Childrens Hospital not available for request.